Reliable and Lightweight Adaptive Convolution Network for PCB Surface Defect Detection

Surface defect detection is very important for the printed circuit board (PCB) to ensure their quality requirements. This article proposes a reliable and lightweight adaptive convolution (LAC) network for PCB surface defect detection. First, an automated optical inspection (AOI) for collecting PCB defects is introduced, and the formation mechanism of PCB defects is systematically analyzed. After that, LAC strategically aggregates multiple convolution kernels and simplifies model complexity through tensor decomposition. Furthermore, the confidence gate learning (CGL) strategy aims to cope with dataset noise by combining collaborative learning (CL) and confidence evaluation. Complexity and convergence analyses support the theoretical basis of the method. Finally, three industrial defect datasets are used to evaluate the effectiveness. The results show that the methodology has powerful feature representation, visual interpretability, and detection robustness.